1. Field Of The Invention
The present invention pertains to neural networks and to electronic circuits for performing the learning function. More particularly, the present invention pertains to circuits for adjusting a synaptic weight based on the product of an input signal and an error signal.
2. The Prior Art
Many schemes have been proposed to use floating gate structures as weight storage for analog neural networks. Any such network requires a synaptic update mechanism which allows the weight to be changed depending on the combination of an input signal and an error signal. The most popular update rules currently in use implement some form of gradient descent, in which the weight is decreased when the input is of the same sign as the error, and is increased when the input has a sign opposite to that of the error. This form of learning is thus inherently a four quadrant computation. The desirable properties of such an update mechanism when implemented in an analog integrated circuit are small size, ability to work continuously (i.e., to use the signal while it is being updated), and freedom from high-voltage circuitry requirements within the synapse cell itself. The learning rate of such an update mechanism should not vary widely between circuits on the same chip.
Accordingly, it is an object of the present invention to provide a continuous weight-update apparatus for use with a synapse in a neural network.
Another object of the present invention is to provide an encoding of input and error signals, and a circuit for using these encoded signals to compute the control signals for a tunneling structure and a hot-electron injection structure to provide a continuous weight update apparatus.
A further object of the present invention is to provide a neural network including a plurality of synapses, each of the synapses including a continuous weight update-device.
This and other objects of the invention will be apparent to any person of ordinary skill in the art from the description of the invention contained herein.